In an automated manufacturing facility, optimal operation of the facility depends on individual manufacturing cells or stations (workcells or workstations) processing parts or workpieces at their respective expected production rate. Workstations not operating at their expected production rate generally constrain product flow and affects overall throughput of the manufacturing process. It is Generally believed that throughput of the facility is limited by its constraints, namely, workstations causing relatively most significant disruption to the production or workflow of the facility. Continuous improvement efforts will likely be hindered if one is unable to determine the constraints in an automated manufacturing facility.
Locating the constraints, however, is not an easy task. This may be particularly so in a facility with sequential production lines. As is known, an automated manufacturing facility often consists of a number of sequential production or processing lines. Each sequential production line consists of several automated workstations linked in a sequential manner. Each of the automated workstations processes a workpiece at a fixed pace as the workpiece moves sequentially from one workstation to the next in the line. In a sequential production line, slowdown at any one of the workstations often causes a ripple effect that spreads both upstream and downstream from the source. Slowdowns can be attributed to many different causes. “Slow cycles”, or losses due to a workstation running at a slower than the expected pace, can be one of the contributing factors to the slowdown of a production line. There may also be quality defects, causing processed workpieces being discarded, thus creating gaps in the workflow. Of course, there can always be downtime losses at each workstation, which not only affects the performance at the workstation in question but also creates starved conditions downstream and blocked conditions upstream. As a result, these bottlenecks and quality issues can ripple through the sequential production line and they tend to mask the source of constraints. For example, a workstation may be idle and appear to be causing performance issues but only because it is starved or blocked due to problems upstream or downstream. Buffers between workstations or production lines are often provided to level workflow in order to achieve continuous proceeding or production. Buffering may further mask the real source of constraints.
Metrics are generally used to measure productivity of a manufacturing facility and to measure effectiveness of changes made to workstations or manufacturing environment. Typical approaches to solving the problem of improving performance or productivity in a manufacturing environment generally do not deploy methods of identifying constraints. Instead, these methods focus on collecting a different set of data such as machine faults, overall equipment efficiency (“OEE”), idle time, and scrap rates. These metrics, while providing an indication of the overall performance of a plant, generally do not provide satisfactory guidance as to where a problem may have originated and therefore assist management in locating specific workstations where corrective measures may be required.
To compound the difficulty, often, more and more data are collected in an attempt to improve predictive power of a chosen metric. This information is collected from all workstations in a manufacturing facility. Unfortunately, often a user is left to interpret this increased amount of data to try to identify where constraints in the manufacturing process exist. Because of the complexity of the data and the concurrency of information, this often is difficult. Additionally, there are also disadvantages generally associated with collecting and interpreting a large amount of data, which will only be exacerbated when more data are collected than necessary. It tends to be time consuming and expensive to implement a solution that requires a large amount of data. Any solution deployed also needs to correctly reflect the nature of the data collected. This may call for extra time and effort to understand all of the data and the interrelationship among the types of data collected, which may not be a trivial task. It is therefore desirable to collect only minimally necessary data that still can lead to correct identification of constraints.
There is therefore a need for a system and method for collecting minimum amount of data, i.e., only those relevant to identifying correctly constraints that limit the throughput, and processing the data collected to identify the constraints, and for providing a concise and clear view of where constraints are. It is an object of the present invention to mitigate or obviate at least one of the above mentioned disadvantages.